Field-based Robotic Phenotyping for Sorghum Biomass Yield Component Traits Characterization Using Stereo Vision Bao, Yin Tang, Lie Tang, Lie
dc.contributor.department Agricultural and Biosystems Engineering
dc.contributor.department Human Computer Interaction
dc.contributor.department Plant Sciences Institute 2019-12-09T21:48:24.000 2020-06-29T22:36:34Z 2020-06-29T22:36:34Z Fri Jan 01 00:00:00 UTC 2016 2016-01-01
dc.description.abstract <p>Sorghum is known as a major potential feedstock for biofuel production. Being able to efficiently discover genetic control of many traits over a large number of genotypes, genome-wide association study (GWAS) has become a powerful tool for studying sorghum biomass yield components. However, automated high-throughput field-based plant phenotyping is now the bottleneck for scaling up such experiments. This paper presents an auto-guidance enabled utility tractor which navigates itself between crop rows with a predefined path while collecting stereo images of sorghum samples from both sides of the vehicle. Three levels of stereo camera heads were instrumented to capture images of plants up to 3 meters tall. The stereo images were processed offline to reconstruct 3D point clouds using Semi-Global Block Matching. A semi-automated software interface was developed to measure stem diameter due to the strict sampling strategy and the complexity of high-density crop canopy. An automated hedge-based feature extraction pipeline was proposed to quantify other variations in plant architecture traits such as plant height, leaf area index (LAI) and vegetation volume index (VVI). The stem diameter measured using the semiautomatic method showed high correlation (0.958) to hand measurement.</p>
dc.description.comments <p>This article is published as Bao, Yin, and Lie Tang. "Field-based robotic phenotyping for sorghum biomass yield component traits characterization using stereo vision." <em>IFAC-PapersOnLine</em> 49, no. 16 (2016): 265-270. DOI: <a href="" target="_blank">10.1016/j.ifacol.2016.10.049</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 2346
dc.identifier.contextkey 15362167
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/1060
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 18:24:39 UTC 2022
dc.source.uri 10.1016/j.ifacol.2016.10.049
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Field phenotyping
dc.subject.keywords biomass sorghum
dc.subject.keywords plant height
dc.subject.keywords stem diameter
dc.subject.keywords leaf area
dc.subject.keywords vegetation volume
dc.subject.keywords semi-global stereo matching
dc.title Field-based Robotic Phenotyping for Sorghum Biomass Yield Component Traits Characterization Using Stereo Vision
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication e60e10a5-8712-462a-be4b-f486a3461aea
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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